October 2024 arXiv papers — page 49

Mohsen Ghaffari, Christoph Grunau

A recent work by Christiansen, Nowicki, and Rotenberg provides dynamic algorithms for coloring sparse graphs, concretely as a function of the arboricity alpha of the input graph. They give two randomized algorithms: O({alpha} log {alpha}) implicit coloring in poly(log n) worst-case update and query times, and O(min{{alpha} log {alpha}, {alpha} log log log n}

Branko Mitic, Philipp Seeböck, Jennifer Straub, Helmut Prosch

Fast detection of emerging diseases is important for containing their spread and treating patients effectively. Local anomalies are relevant, but often novel diseases involve familiar disease patterns in new spatial distributions. Therefore, established local anomaly detection approaches may fail to identify them as new. Here, we present a novel approach to

Jan Forsman, Clifford E. Woodward

In this communication we demonstrate the existence of a first-order prewetting transition of a supracritical model polymer solution adjacent to an attractive surface. The model fluid we use mimics (qualitatively) an aqueous polyethylene oxide solution and, like the actual solution, displays a closed loop 2-phase region with an upper and lower critical soluti

Christian Saugbjerg Lange, Lars Bojer Madsen

The quantum optical description of high-order harmonic generation where both the electrons of the generating medium and the driving and generated light fields are described quantum mechanically has been of significant interest in the past years. The quantum optical formulation leads to equations of motion for the generated light field in which the quantum op

Florian Nill

In SIR-type epidemic models time derivative of prevalence $I$ can always be cast into the form $\dot{I}=(X-1)I$, where $X$ is the replacement number and recovery rate is normalized to one. Assuming $\dot{X}=f(X,I)$ for some smooth function $f$ defines a "replacement number dynamics" (RND). Choosing transmission coefficients $\beta_1>\beta_2$, any such system

Pengpeng Cheng, Tongzhu Li

Let $M^4\to \mathbb{S}^5$ be a closed immersed minimal hypersurface with constant squared length of the second fundamental form $S$ in a $5$-dimensional sphere $\mathbb{S}^5$. In this paper, we prove that if $3$-mean curvature $H_3$ and the number $g$ of the distinct principal curvatures are constant, then $M^4$ is an isoparametric hypersurface, and the valu

Ilaria Bertoni, Aldo Ugolotti, Emilio Scalise, Roberto Bergamaschini

Despite the extensive work carried out on the epitaxial growth of Ga2O3, a fundamental understanding of the nucleation of its different metastable phases is still lacking. Here we address the role of interface energies by Density Functional Theory calculations of alpha, beta and kappa Ga2O3 on (0001) Al2O3 substrates, and different Ga2O3 interlayers. In conj

Adnan Ebrahem, Jannes Hohl, Etienne Jessen, Marco F. P. ten Eikelder

We present a framework for modeling liver regrowth on the organ scale that is based on three components: (1) a multiscale perfusion model that combines synthetic vascular tree generation with a multi-compartment homogenized flow model, including a homogenization procedure to obtain effective parameters; (2) a poroelastic finite growth model that acts on all

Francisco Erivaldo Fernandes Junior, Antti Oulasvirta

Developing a reinforcement learning (RL) agent often involves identifying values for numerous parameters, covering the policy, reward function, environment, and agent-internal architecture. Since these parameters are interrelated in complex ways, optimizing them is a black-box problem that proves especially challenging for nonexperts. Although existing optim

Clemens Paul Zengler, Niels Troldborg, Mac Gaunaa

Streamwise acceleration of the background flow field is one of various effects occurring when wind turbines operate under non-idealized conditions, such as in complex terrain or in dense wind farms. Thus, studying this effect is essential to improve understanding of aerodynamic performance in these cases. In the present work, a simple model based on momentum

Bing Wu, Xiang-Kun Dong, Meng-Lin Du, Feng-Kun Guo

The low-energy $J/\psi N$ scattering is important for various reasons: it is related to the hidden-charm $P_c$ pentaquark states, provides insights into the role of gluons in nucleon structures, and is relevant to the $J/\psi$ properties in nuclear medium. The scattering can happen through two distinct mechanisms: the coupled-channel mechanism via open-charm

Marius Duvillard, Loïc Giraldi, Olivier Le Maître

This study presents a novel approach to applying data assimilation techniques for particle-based simulations using the Ensemble Kalman Filter. While data assimilation methods have been effectively applied to Eulerian simulations, their application in Lagrangian solution discretizations has not been properly explored. We introduce two specific methodologies t

Tanja Dravec, Mirjana Mikalački, Andrej Taranenko

A span of a given graph $G$ is the maximum distance that two players can keep at all times while visiting all vertices (edges) of $G$ and moving according to certain rules, that produce different variants of span. We prove that the vertex and edge span of the same variant can differ by at most 1 and present a graph where the difference is exactly 1. For all

Mitra Ebrahimpoor, Renee Menezes, Ningning Xu, Jelle J. Goeman

Integrated analysis of multi-omics datasets holds great promise for uncovering complex biological processes. However, the large dimension of omics data poses significant interpretability and multiple testing challenges. Simultaneous Enrichment Analysis (SEA) was introduced to address these issues in single-omics analysis, providing an in-built multiple testi

Eitan Farchi, Debbie Furman

Typical software has a huge input space. The number of inputs may be astronomical or even infinite. Thus, the task of validating that the software is correct seems hopeless. To deal with this difficult task, Combinatorial Test Design (CTD) can be used to provide reduction of the testing space and high quality and efficient testing. The application of CTD is

Stefano Longhi

Mosaic lattice models have been recently introduced as a special class of disordered systems displaying resonance energies, multiple mobility edges and anomalous transport properties. In such systems on-site potential disorder, either uncorrelated or incommensurate, is introduced solely at every equally-spaced sites within the lattice, with a spacing $M \geq

Thorsten Altenkirch, Jacob Neumann

The field of directed type theory seeks to design type theories capable of reasoning synthetically about (higher) categories, by generalizing the symmetric identity types of Martin-L\"of Type Theory to asymmetric hom-types. We articulate the directed type theory of the category model, with appropriate modalities for keeping track of variances and a powerful

Ondřej Faltus, Milan Jirásek, Martin Horák, Martin Doškář

Pattern-forming metamaterials feature microstructures specifically designed to change the material's macroscopic properties due to internal instabilities. These can be triggered either by mechanical deformation or, in the case of active materials, by other external stimuli, such as pneumatic actuation. We study a two-dimensional rectangular lattice microstru

Max Goldberg, Konstantin Batygin

While numerous planetary and asteroid satellites show evidence for non-trivial rotation states, none are as emblematic as Hyperion, which has long been held as the most striking example of chaotic spin-orbit evolution in the Solar System. Nevertheless, an analytically tractable theory of the full 3D spin-orbit dynamics of Hyperion has not been developed. We

Muhammad Zain Ali, Yuxia Wang, Bernhard Pfahringer, Tony Smith

The rise of social media has amplified the spread of fake news, now further complicated by large language models (LLMs) like ChatGPT, which ease the generation of highly convincing, error-free misinformation, making it increasingly challenging for the public to discern truth from falsehood. Traditional fake news detection methods relying on linguistic cues a

Mohsen Ghaffari, Christoph Grunau

This paper improves and in two cases nearly settles, up to logarithmically lower-order factors, the deterministic complexity of some of the most central problems in distributed graph algorithms, which have been studied for over three decades: Near-Optimal Network Decomposition: We present a deterministic distributed algorithm that computes a network decompos

Georgios Marangelis

The main goal of this paper is to generalize the results that where presented in [11] for $\aleph_1$-Kurepa trees to $\aleph_{\alpha+1}$-Kurepa trees. We construct an $\mathcal{L}_{\omega_1,\omega}$-sentence $\psi_{\alpha}$, that codes $\aleph_{\alpha+1}$-Kurepa trees, for some countable $\alpha$. One of the main results for its spectrum is the following: It

Gabriele Immordino, Andrea Da Ronch, Marcello Righi

This study introduces an approach for modeling unsteady transonic aerodynamics within a parametric space, using Volterra series to capture aerodynamic responses and machine learning to enable interpolation. The first- and second-order Volterra kernels are derived from indicial aerodynamic responses obtained through computational fluid dynamics, with the seco

Katsuhiro Tanaka, Takuya Nomoto, Ryotaro Arita

The tunnel magnetoresistance (TMR) effect is one of the representative phenomena in spintronics. Ferromagnets, which have a net spin polarization, have been utilized for the TMR effect. Recently, by contrast, the TMR effect with antiferromagnets, which do not possess a macroscopic spin polarization, has been proposed, and also been observed in experiments. I

Hui Chen, Xuhui Fan, Hengyu Liu, Longbing Cao

Marked event data captures events by recording their continuous-valued occurrence timestamps along with their corresponding discrete-valued types. They have appeared in various real-world scenarios such as social media, financial transactions, and healthcare records, and have been effectively modeled through Marked Temporal Point Process (MTPP) models. Recen

Francesco Marino, Francesca Bonaiti, Sonia Bacca, Pepijn Demol

In this contribution, we report on recent progress in coupled-cluster simulations of open-shell atomic nuclei using interactions consistently derived from chiral effective field theory. In particular, we compare different coupled-cluster approaches by computing binding energies and electric dipole polarizabilities in medium-mass calcium isotopes.

Karim Essalmi, Fernando Garrido, Fawzi Nashashibi

Decision-making for automated driving remains a challenging task. For their integration into real platforms, these algorithms must guarantee passenger safety and comfort while ensuring interpretability and an appropriate computational time. To model and solve this decision-making problem, we have developed a novel approach called COR-MP (Conservation of Reso

Femke Pfaue

SSP reductions are a type of polynomial reductions that also preserve the solutions of the instances. This means there is a mapping from each solution in the original instance to one in the reduced instance, allowing direct deduction of an original solution from a solution in the reduced instance. SSP reductions can be used to show SSP-NP completeness of a p

M. Ghani Varzaneh, F. Z. Lahbiri, S. Riedel

In this paper, we develop a way of analyzing the random dynamics of stochastic evolution equations with a non-dense domain. Such problems cover several types of evolution equations. We are particularly interested in evolution equations with non-homogeneous boundary conditions of white noise type. We prove the existence of stable, unstable, and center manifol

Ehsan Pashay Ahi, Morgane Frapin, Mikaela Hukkanen, Craig R. Primmer

Better understanding the molecular processes contributing to variation in maturation timing is important for Atlantic salmon aquaculture, as early maturation causes considerable financial losses. The m6A RNA methylation is a conserved and dynamically reversible mechanism controlling gene expression in a myriad of biological processes. The role of m6A methyla

Simone Ragoni

Outreach and communication with the public is an integral part of our work as researchers. A wide range of activities and platforms allow ALICE members to share, especially with the young generation, the excitement of our field. ALICE Masterclasses for high-school students, both in-person and online, are expanding, reaching a higher number of students every

Charles Dossal, Samuel Hurault, Nicolas Papadakis

These notes focus on the minimization of convex functionals using first-order optimization methods, which are fundamental in many areas of applied mathematics and engineering. The primary goal of this document is to introduce and analyze the most classical first-order optimization algorithms. We aim to provide readers with both a practical and theoretical un

Endre S. Rundsveen, Laertis Vaso

Support $\tau$-tilting pairs, functorially finite torsion classes and $2$-term silting complexes are three much studied concepts in the representation theory of finite-dimensional algebras, which moreover turn out to be connected via work of Adachi, Iyama and Reiten. We investigate their higher-dimensional analogues via $\tau_d$-rigid pairs, $d$-torsion clas

Zelin Zang, Yuhao Wang, Jinlin Wu, Hong Liu

Dimensionality reduction (DR) plays a crucial role in various fields, including data engineering and visualization, by simplifying complex datasets while retaining essential information. However, achieving both high DR accuracy and strong explainability remains a fundamental challenge, especially for users dealing with high-dimensional data. Traditional DR m

Jahyun Koo, Yerin Hwang, Yongil Kim, Taegwan Kang

Despite the success of Large Language Models (LLMs), they still face challenges related to high inference costs and memory requirements. To address these issues, Knowledge Distillation (KD) has emerged as a popular method for model compression, with student-generated outputs (SGOs) as training data being particularly notable for reducing the mismatch between

Md Abu Talhamainuddin Ansary

In this paper, a globally convergent trust region proximal gradient method is developed for composite multi-objective optimization problems where each objective function can be represented as the sum of a smooth function and a nonsmooth function. The proposed method is free from any kind of priori chosen parameters or ordering information of objective functi

Annabelle Bohrdt, David Wei, Daniel Adler, Kritsana Srakaew

The interplay of spin and charge degrees of freedom is believed to underlie various unresolved phenomena in strongly correlated systems. Quantum simulators based on neutral atoms provide an excellent testbed for investigating such phenomena and resolving their microscopic origins. Up to now, the majority of experimental and theoretical studies has focused on

Anthony Cui, Pranav Nandyalam, Andrew Rufail, Ethan Cheung

Momentum-Aided Prompt Optimization (MAPO) enhances the efficiency and efficacy of prompt optimization for Large Language Models (LLMs). Building on ProTeGi, MAPO uses positive natural language "gradients" and a momentum-based extension to refine prompts effectively. By tracking gradient history, MAPO avoids local minima and oscillations. It also utilizes bea

Parvez Ali, Annmaria Baby, D. Antony Xavier, Eddith Sarah Varghese

The modern era always looks into advancements in technology. Design and topology of interconnection networks play a mutual role in development of technology. Analysing the topological properties and characteristics of an interconnection network is not an easy task. Graph theory helps in solving this task analytically and efficiently through the use of numeri

Sjoerd Terpstra, Swinda K. J. Falkena, Robbin Bastiaansen, Sebastian Bathiany

Past research has shown that multiple climate subsystems might undergo abrupt shifts, such as the Arctic Winter sea ice or the Amazon rainforest, but there are large uncertainties regarding their timing and spatial extent. In this study we investigated when and where abrupt shifts occur in the latest generation of earth system models (CMIP6) under a scenario

Xavier Mestre, Adrian Agustin, David Sarda

The capabilities of multi-antenna technology have recently been significantly enhanced by the proliferation of extra large array architectures. The high dimensionality of these systems implies that communications take place in the nearfield regime, which poses some questions as to their effective perfomrance even under simple line of sight configurations. In

Roel Hacking, Lisa Kusch, Koondanibha Mitra, Martijn Anthonissen

This paper introduces a novel neural network-based approach to solving the Monge-Amp\`ere equation with the transport boundary condition, specifically targeted towards optical design applications. We leverage multilayer perceptron networks to learn approximate solutions by minimizing a loss function that encompasses the equation's residual, boundary conditio

Fabio Morea, Domenico De Stefano

This article explores the importance of examining the solution space in community detection, highlighting its role in achieving reliable results when dealing with real-world problems. A Bayesian framework is used to estimate the stability of the solution space and classify it into categories Single, Dominant, Multiple, Sparse or Empty. By applying this appro

Christos Xypolopoulos, Guokan Shang, Xiao Fei, Giannis Nikolentzos

Large language models have evolved to process multiple modalities beyond text, such as images and audio, which motivates us to explore how to effectively leverage them for graph reasoning tasks. The key question, therefore, is how to transform graphs into linear sequences of tokens, a process we term "graph linearization", so that LLMs can handle graphs natu

Till Aczel, Roger Wattenhofer

In lossy image compression, models face the challenge of either hallucinating details or generating out-of-distribution samples due to the information bottleneck. This implies that at times, introducing hallucinations is necessary to generate in-distribution samples. The optimal level of hallucination varies depending on image content, as humans are sensitiv

Stefan Wahl, Armand Rousselot, Felix Draxler, Henrik Schopmans

Modeling distributions that depend on external control parameters is a common scenario in diverse applications like molecular simulations, where system properties like temperature affect molecular configurations. Despite the relevance of these applications, existing solutions are unsatisfactory as they require severely restricted model architectures or rely

Jan Kożuszek, Toby Wiseman

Ghost-free dRGT massive gravity is a subtle theory, even at the classical level. Its viability depends on Vainshtein screening, which is an intrinsically non-linear phenomenon, and thus understanding the full non-linear dynamics of the theory is crucial. The theory was not expected to have a well-posed hyperbolic formulation as it is usually interpreted as a

Alberto Pipitone Federico

We prove that the general fiber of a compact hypercomplex twistor space with a K\"{a}hler fiber has no divisors nor curves. This is first used to prove that, under the same assumption, the trascendental degree of the field of meromoprhic functions is one. The same result allows to prove that these spaces admit no K\"{a}hler and not even pluriclosed metrics.

Noé Olivier, Michel Nowak

Monte Carlo particle transport codes are well established on classical hardware and are considered as the reference tool for nuclear applications. In a growing number of domains, the design of algorithms is progressively shifting towards the field of quantum computing, where theoretical speedups over their classical counterparts are expected. In some of thes

Xin Shen, Heming Du, Hongwei Sheng, Shuyun Wang

Isolated Sign Language Recognition (ISLR) focuses on identifying individual sign language glosses. Considering the diversity of sign languages across geographical regions, developing region-specific ISLR datasets is crucial for supporting communication and research. Auslan, as a sign language specific to Australia, still lacks a dedicated large-scale word-le

Swetlana Hubrig, Markus Schöller, Silva P. Järvinen, Aleksandar Cikota

Magnetic fields are considered to be key components of massive stars, with a far-reaching impact on their evolution and ultimate fate. A magnetic mechanism was suggested for the collimated explosion of massive stars, relevant for long-duration gamma-ray bursts, X-ray flashes, and asymmetric core collapse supernovae. However, the origin of the observed stable

Vivek Parmar, Dwijay Bane, Syed Shakib Sarwar, Kleber Stangherlin

With the emergence of the Metaverse and focus on wearable devices in the recent years gesture based human-computer interaction has gained significance. To enable gesture recognition for VR/AR headsets and glasses several datasets focusing on egocentric i.e. first-person view have emerged in recent years. However, standard frame-based vision suffers from limi

Ray Li, Tanishka Bagade, Kevin Martinez, Flora Yasmin

Large language models (LLMs) have achieved a degree of success in generating coherent and contextually relevant text, yet they remain prone to a significant challenge known as hallucination: producing information that is not substantiated by the input or external knowledge. Previous efforts to mitigate hallucinations have focused on techniques such as fine-t

Ed Bennett, Andreas Athenodorou, Georg Bergner, Pietro Butti

The family of SU(2) theories with matter transforming in the adjoint representation has attracted interest from many angles. The two-flavour theory, known as Minimal Walking Technicolor, has a body of evidence pointing to it being in the conformal window with anomalous dimension $\gamma_{*}\approx0.3$. Perturbative calculations would suggest that the one-fla

Weihang Liu, Xue Xian Zheng, Jingyi Yu, Xin Lou

The recent popular radiance field models, exemplified by Neural Radiance Fields (NeRF), Instant-NGP and 3D Gaussian Splatting, are designed to represent 3D content by that training models for each individual scene. This unique characteristic of scene representation and per-scene training distinguishes radiance field models from other neural models, because c

Jamie Hayes, Marika Swanberg, Harsh Chaudhari, Itay Yona

Large language models (LLMs) are susceptible to memorizing training data, raising concerns about the potential extraction of sensitive information at generation time. Discoverable extraction is the most common method for measuring this issue: split a training example into a prefix and suffix, then prompt the LLM with the prefix, and deem the example extracta

Candy Sonveaux, Christophe Prieur, Gildas Besançon, Joseph J. Winkin

An age-structured Susceptible-Infected-Recovered-Deceased (SIRD) epidemic model is considered. The aim of this paper is to design an observer-based output feedback control law, representing an immunization process, typically vaccination, intended to decrease the peak of infected individuals in the population. At first, well-posedness and stability of the sys

N. Gaspari, H. F. Stevance, A. J. Levan, A. A. Chrimes

Aims. The locations of binary neutron star (BNS) mergers within their host galaxies encode the systemic kicks that these systems received in the supernova aftermath. We investigate how the galactic potential and the systemic kicks shape the offset distribution of BNS mergers with a case study of GW 170817 and its host NGC 4993. Methods. We derived dynamical

Nuthan Mummani, Simran Ketha, Venkatakrishnan Ramaswamy

In the supervised classification setting, during inference, deep networks typically make multiple predictions. For a pair of such predictions (that are in the top-k predictions), two distinct possibilities might occur. On the one hand, each of the two predictions might be primarily driven by two distinct sets of entities in the input. On the other hand, it i

Micha C. J. Philipp, Yannick Kuhn, Arnulf Latz, Birger Horstmann

To further improve Lithium-ion batteries (LiBs), a profound understanding of complex battery processes is crucial. Physical models offer understanding but are difficult to validate and parameterize. Therefore, automated machine-learning methods (ML) are necessary to evaluate models with experimental data. Bayesian methods, e.g., Bayesian optimization for lik

B. Neukelmance, B. Hue, Q. Schaeverbeke, L. Jarjat

Semiconductor quantum dots are an attractive platform for the realisation of quantum processors. To achieve long-range coupling between them, quantum dots have been integrated into microwave cavities. However, it has been shown that their coherence is then reduced compared to their cavity-free implementations. Here, we manipulate the quantum states of a susp

Mathis Caprais, André Bergeron, Nathan Greiner, Daniele Tomatis

In nuclear reactors, Delayed Neutron Precursors (DNPs) are important for reactor safety and operation. In liquid nuclear fuels, DNPs are transported by the flow, and an advection-reaction balance equation for their concentration must be solved in addition to the Neutron Balance Equation. This research paper applies the method of characteristics to solve the

Takuma Nishimura, Andreea Dogaru, Martin Oeggerli, Bernhard Egger

Scanning Electron Microscopes (SEMs) are widely renowned for their ability to analyze the surface structures of microscopic objects, offering the capability to capture highly detailed, yet only grayscale, images. To create more expressive and realistic illustrations, these images are typically manually colorized by an artist with the support of image editing

Isabel Santos-Santos, Carlos Frenk, Julio Navarro, Shaun Cole

We study the abundance, radial distribution, and orbits of luminous satellites in simulations of MW-mass dark halos in the LCDM cosmology. We follow the evolution of a halo from the Aquarius project and the formation of its maximal satellite population with the GALFORM semi-analytic model of galaxy formation. This population consists of all subhalos able to

Benjamin Buchholz

Plasma current instabilities can destabilize the plasma discharge and cool the plasma rapidly. In such $\textit{disruptions}$ or in the start-up phase of the reactor, inductive electric fields are generated which accelerate electrons to relativistic velocities, resulting in a beam of $\textit{runaway electrons}$. This can potentially damage the reactor vesse

Changshi Mu, Daquan Feng, Qi Zheng, Yuan Zhuang

Accurate and robust initialization is essential for Visual-Inertial Odometry (VIO), as poor initialization can severely degrade pose accuracy. During initialization, it is crucial to estimate parameters such as accelerometer bias, gyroscope bias, initial velocity, gravity, etc. Most existing VIO initialization methods adopt Structure from Motion (SfM) to sol

Domagoj Bošnjak, Richard Schussnig, Sascha Ranftl, Gerhard A. Holzapfel

In the context of numerical simulations of the vascular system, local geometric uncertainties have not yet been examined in sufficient detail due to model complexity and the associated large numerical effort. Such uncertainties are related to geometric modeling errors resulting from computed tomography imaging, segmentation and meshing. This work presents a

Ryan Park, Darren J. Hsu, C. Brian Roland, Maria Korshunova

Inverse folding models play an important role in structure-based design by predicting amino acid sequences that fold into desired reference structures. Models like ProteinMPNN, a message-passing encoder-decoder model, are trained to reliably produce new sequences from a reference structure. However, when applied to peptides, these models are prone to generat

Charles M. Elliott, Achilleas Mavrakis

We consider the surface Stokes equation with Lagrange multiplier and approach it numerically. Using a Taylor-Hood surface finite element method, along with an appropriate estimate for the additional Lagrange multiplier, we derive a new inf-sup condition to help with the stability and convergence results. We establish optimal velocity convergence both in ener

András Telcs, Marcell T. Kurbucz, Antal Jakovác

Temporally evolving systems are typically modeled by dynamic equations. A key challenge in accurate modeling is understanding the causal relationships between subsystems, as well as identifying the presence and influence of unobserved hidden drivers on the observed dynamics. This paper presents a unified method capable of identifying fundamental causal relat

Tanvir Kaur, Barun Gorain, Kaushik Mondal

Distance-2-Dispersion (D-2-D) problem aims to disperse $k$ mobile agents starting from an arbitrary initial configuration on an anonymous port-labeled graph $G$ with $n$ nodes such that no two agents occupy adjacent nodes in the final configuration, though multiple agents may occupy a single node if there is no other empty node whose all adjacent nodes are a

Carlo Novara, Mattia Boggio, Deborah Volpe

Nonlinear Model Predictive Control (NMPC) is a general and flexible control approach, used in many industrial contexts, and is based on the online solution of a nonlinear optimization problem. This operation requires in general a high computational cost, which may compromise the NMPC implementation in ``fast'' applications, especially if a large number varia

Yan Wang, Niklas Grabicki, Hibiki Orio, Juan Li

The fabrication of well-defined, low-dimensional diamondoid-based materials is a promising approach for tailoring diamond properties such as superconductivity. On-surface self-assembly of halogenated diamondoids under ultrahigh vacuum conditions represents an effective strategy in this direction, enabling reactivity exploration and on-surface synthesis appro

Marten Berger, Gudrid Moortgat-Pick, Monika Wüst

The possibility of a Photon-Photon collider extension to the Beam dump of the $17.5$ GeV European XFEL has been discussed before as the first high energy collider of its sort. It would not just be to study the concept of photon colliders but would also be a collider without competition in the region of $5 - 12$ GeV for photon-photon collision. In this range,

Jiajun Zhang, Boyang Qiang, Xiaoyu Guo, Weiwei Xing

Discovering the underlying Directed Acyclic Graph (DAG) from time series observational data is highly challenging due to the dynamic nature and complex nonlinear interactions between variables. Existing methods typically search for the optimal DAG by optimizing an objective function but face scalability challenges, as their computational demands grow exponen

Sudarshan Ananth, Nipun Bhave

We construct maximal supergravity in five-dimensions by 'oxidizing' the four-dimensional $\mathcal{N}=8$ theory. The relevant symmetries, the unitary symplectic group $USp(8)$ and the exceptional group $E_6$, are both presented in light-cone superspace and their connections with $SU(8)$ and $E_7$ highlighted. We explain a procedure to derive higher-point int

Dušan Popov

We defined and used a pair of Hermitian annihilation and creation operators which generate the generalized coherent states, defined in the Barut-Girardello manner, whose normalization function is just the four-parameter generalized Mittag-Leffler function. We examined the characteristic properties for these pure, as well as mixed (thermal) coherent states. A

Xuetian Chen, Hangcheng Li, Jiaqing Liang, Sihang Jiang

Autonomous agents operating on the graphical user interfaces (GUIs) of various applications hold immense practical value. Unlike the large language model (LLM)-based methods which rely on structured texts and customized backends, the approaches using large vision-language models (LVLMs) are more intuitive and adaptable as they can visually perceive and direc

Saleem Abdul Fattah Ahmed Al Dajani, David E. Keyes

We present a novel approach for accelerating AI performance by leveraging Anderson extrapolation, a vector-to-vector mapping technique based on a window of historical iterations. By identifying the crossover point (Fig. 1) where a mixing penalty is incurred, the method focuses on reducing iterations to convergence, with fewer more compute-intensive but gener

Pedro Martin, António Rodrigues, João Ascenso, Maria Paula Queluz

Neural Radiance Fields (NeRF) have revolutionized the field of 3D visual representation by enabling highly realistic and detailed scene reconstructions from a sparse set of images. NeRF uses a volumetric functional representation that maps 3D points to their corresponding colors and opacities, allowing for photorealistic view synthesis from arbitrary viewpoi

Haojin Li, Xiaodong Cheng, Peter van Heijster, Sitian Qin

In this paper, we present an event-triggered distributed optimization approach including a distributed controller to solve a class of distributed time-varying optimization problems (DTOP). The proposed approach is developed within a distributed neurodynamic (DND) framework that not only optimizes the global objective function in real-time, but also ensures t

Vladimir V. Belokurov, Vsevolod V. Chistiakov, Evgeniy T. Shavgulidze

The action $A$ of Quadratic Gravity in FLRW metric is invariant under the group of diffeomorphisms of the time coordinate and can be written in terms of the only dynamical variable $g(\tau)\,.$ We construct perturbation theory for calculating path integrals of the form $\int\,F(g)\,\exp\left\{-A (g)\right\}dg\,,$ and find the averaged value of the scale fact

Saleh Ashkboos, Iman Mirzadeh, Keivan Alizadeh, Mohammad Hossein Sekhavat

While large language models (LLMs) dominate the AI landscape, Small-scale large Language Models (SLMs) are gaining attention due to cost and efficiency demands from consumers. However, there is limited research on the training behavior and computational requirements of SLMs. In this study, we explore the computational bottlenecks of training SLMs (up to 2B p

Joao Gouveia, Fernando Branco, Armanda Rodrigues, Nuno Correia

Project Lx Conventos aims to study, in a systematic and integrated manner, the impact of the dissolution of religious orders in the dynamics of urban transformation in nineteenth century Lisbon. After the liberal revolution and the civil war, in the 19th century, the dissolution of religious orders led to the alienation, in Lisbon, of nearly 130 religious bu

Milan Studený

Five different ways of combinatorial description of non-empty faces of the cone of supermodular functions on the power set of a finite basic set $N$ are introduced. Their identification with faces of the cone of supermodular games allows one to associate to them certain polytopes in $\mathbb{R}^{N}$, known as cores (of these games) in context of cooperative

Hengyuan Zhang, Chenming Shang, Sizhe Wang, Dongdong Zhang

Although fine-tuning Large Language Models (LLMs) with multilingual data can rapidly enhance the multilingual capabilities of LLMs, they still exhibit a performance gap between the dominant language (e.g., English) and non-dominant ones due to the imbalance of training data across languages. To further enhance the performance of non-dominant languages, we pr

Zixuan Gong, Guangyin Bao, Qi Zhang, Zhongwei Wan

Reconstruction of static visual stimuli from non-invasion brain activity fMRI achieves great success, owning to advanced deep learning models such as CLIP and Stable Diffusion. However, the research on fMRI-to-video reconstruction remains limited since decoding the spatiotemporal perception of continuous visual experiences is formidably challenging. We conte

Yirui Chen, Qinyu Xiao, Jia Yi, Jing Chen

This paper explores the application of prompt engineering to enhance the performance of large language models (LLMs) in the domain of Traditional Chinese Medicine (TCM). We propose TCM-Prompt, a framework that integrates various pre-trained language models (PLMs), templates, tokenization, and verbalization methods, allowing researchers to easily construct an

Hai Zhong, Xun Wang, Zhuoran Li, Longbo Huang

Offline-to-Online Reinforcement Learning has emerged as a powerful paradigm, leveraging offline data for initialization and online fine-tuning to enhance both sample efficiency and performance. However, most existing research has focused on single-agent settings, with limited exploration of the multi-agent extension, i.e., Offline-to-Online Multi-Agent Reinf

Maxence Noble, Louis Grenioux, Marylou Gabrié, Alain Oliviero Durmus

Over the past few years, several approaches utilizing score-based diffusion have been proposed to sample from probability distributions, that is without having access to exact samples and relying solely on evaluations of unnormalized densities. The resulting samplers approximate the time-reversal of a noising diffusion process, bridging the target distributi

Aviral Dhingra

Gradient descent is a widely used iterative algorithm for finding local minima in multivariate functions. However, the final iterations often either overshoot the minima or make minimal progress, making it challenging to determine an optimal stopping point. This study introduces a new efficiency metric, Ek, designed to quantify the effectiveness of each iter

Hao Wu, Yang Huang, Huawei Zhang, Haibo Yuan

We present systematic identifications of supergiants of M31/M33 based on massive LAMOST spectroscopic survey. Radial velocities of nearly 5000 photometrically selected M31/M33 supergiant candidates have been properly derived from the qualified spectra released in LAMOST DR10. By comparing their radial velocities with those predicted from the rotation curves

Yao Wu, Mingwei Xing, Yachao Zhang, Yuan Xie

In cross-modal unsupervised domain adaptation, a model trained on source-domain data (e.g., synthetic) is adapted to target-domain data (e.g., real-world) without access to target annotation. Previous methods seek to mutually mimic cross-modal outputs in each domain, which enforces a class probability distribution that is agreeable in different domains. Howe

Qiujiang Liang, Jun Yang

Simulating water accurately has been a challenge due to the complexity of describing polarization and intermolecular charge transfer. Quantum mechanical (QM) electronic structures provide an accurate description of polarization in response to local environments, which is nevertheless too expensive for large water systems. In this study, we have developed a p

Syed Sameen Ahmad Rizvi, Aryan Seth, Pratik Narang

Automatically recognizing emotional intent using facial expression has been a thoroughly investigated topic in the realm of computer vision. Facial Expression Recognition (FER), being a supervised learning task, relies heavily on substantially large data exemplifying various socio-cultural demographic attributes. Over the past decade, several real-world in-t

Victor L. Chernyak

Mass spectra are calculated for ${\cal N}=1$ SQCD and SQCD-type theories and for softly broken ${\cal N}=2$ SQCD in vacua with unbroken $Z_{(2N_c-N_F)\geq 2}$ symmetry. It is shown that the Seiberg ${\cal N}=1$ duality works, at best, for massless quarks within the conformal window only. Besides: a) the gauge invariant order parameter for scalar quarks is in

Kam Hung Tong

It is a classical result that the set $K\backslash G /B$ is finite, where $G$ is a reductive algebraic group over an algebraically closed field with characteristic not equal to two, $B$ is a Borel subgroup of $G$, and $K = G^{\theta}$ is the fixed point subgroup of an involution of $G$. In this paper, we investigate the affine counterpart of the aforemention

Zain Ahmed Kapadia

We classify which 2-part Young modules in characteristic 2 are uniserial, and which hook Specht modules in characteristic 2 are direct sums of uniserial summands. This is a continuation of the author's previous work [arxiv:2405.02039].

Parisa Rahdan, Elisabeth Zeyen, Marta Victoria

Transition pathways for Europe to achieve carbon neutrality emphasize the need for a massive deployment of solar and wind energy. Global cost optimization would lead to installing most of the renewable capacity in a few resource-rich countries, but policy decisions could prioritize other factors. In this study, we focus on the effect of energy independence o

Cicero S. R. Mendes, Aluizio F. R. Araújo, Lucas R. C. Farias

Constrained multiobjective optimization problems (CMOPs) are commonly found in real-world applications. CMOP is a complex problem that needs to satisfy a set of equality or inequality constraints. This paper proposes a variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE). The novelties in the model include the DE differe